Deep-learning end-to-end autoencoder for the joint mitigation of chromatic dispersion andKerr nonlinearity in optical communication systems

在这个项目中，我们试图通过使用人工智能的方法来提高光通信系统的频谱效率，从而增加覆盖范围和数据速率。我们强调学习的可解释性--通过使用“体系结构模板”的概念来促进--以实现对光通道及其容量的洞察力的推导。乍一看，光纤承诺了似乎无限的带宽，一个结合了低噪声和小衰减系数的静态传播环境。然而，在过去的几十年里，数据速率的指数增长和电路设计的快速发展将占用的带宽和采样率推向了一个工作点，在这个点上，即使是看似完美的光纤也受到了非线性的支配，不能再被忽视，也不能轻易地得到补偿。从工程学的角度来看，这开辟了一个令人兴奋的研究领域，以减轻这种损伤。与此同时，深度学习驱动的通信已经成为一个很有前途和活跃的研究课题，特别是在无线领域。已经证明，通过“自动编码器”以联合方式对发射机和接收机进行端到端学习允许为(几乎)任意信道找到新的信号星座甚至波形，这些新的信号星座甚至波形不限于线性场景，并且以前不能通过传统方法访问。因此，我们被通过非线性光纤发送信号的挑战和端到端学习框架的概念简单性所吸引。基于我们以前在使用神经网络的无线和光通信中的结果，我们寻求提出针对光纤通道的新的体系结构模板和学习概念，以提高单波长通道以及基于波分多路复用的系统的频谱效率、覆盖范围和数据速率。此外，我们还打算利用非线性傅里叶变换来研究新的结构模板(具有学习的信号星座和波形)与基于特征值的光通信的关系，以寻找联合补偿色散和光纤非线性的进一步想法。

In this project, we seek to improve the spectral efficiency of optical communication systems by using methods from artificial intelligence, and, as a result, increase reach and data rates. We put emphasis on the interpretability of the learnings -- facilitated by using the idea of "architectural templates" -- to enable the derivation of insights about the optical channel and its capacity. At first glance, optical fibers promise a seemingly infinite bandwidth, a static propagation environment combined with low noise and small attenuation coefficients. However, in the previous decades, the exponential growth of data-rates and the fast progress in circuit design have pushed the occupied bandwidth and sampling rates towards an operation point where even the seemingly perfect optical fiber is dominated by nonlinearity that cannot be neglected nor compensated easily anymore. From an engineering perspective, this opens up an exciting field of research to mitigate such impairments. At the same time, deep learning-driven communications has become a promising and active research topic, in particular in the wireless domain. It has been shown that end-to-end learning of transmitter and receiver in a joint manner via an "autoencoder" allows to find new signal constellations and even waveforms for (almost) arbitrary channels that are not restricted to linear scenarios, and that have not been accessible via classic methods before. We, thus, are attracted by the challenges of signaling across the nonlinear optical fiber and the conceptual simplicity of the end-to-end learning framework. Based on our previous results in wireless and optical communications using neural networks, we seek to come up with novel architectural templates and learning concepts tailored to the optical fiber channel, for increasing spectral efficiency, reach and data rates over single wavelength channels, as well as over wavelength division multiplex-based systems. Also, we intend to study the relationship of the novel architectural templates (with learned signal constellations and waveforms) to Eigenvalue-based optical communications using the nonlinear Fourier transformation, to find further ideas for jointly compensating chromatic dispersion and fiber nonlinearities.